42 condition-monitoring-machine-learning PhD positions at Cranfield University in United-States
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pressure (HP) turbine blades and HP vanes is still immature. There is a need to understand the effect of AM processing conditions to the properties of the final materials, especially in non-conventional
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, the hydrogen fuel can’t be simply stored within the existing primary structure of the wings but require dedicated tanks. Moreover, due to the cryogenic conditions of the liquid hydrogen on-board, it is also
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operation of autonomous systems in complex, real-world conditions. This PhD project aims to develop resilient Position, Navigation and Timing (PNT) systems for autonomous transport, addressing a critical
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Technology. Mr Kumar is the module leader for Military Vehicle Dynamics, part of the Military Vehicle Technology MSc, which he teaches in the UK and overseas. He worked on project from the UK Ministry of
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lack a direct correlation with process parameters, limiting their ability to predict temperature fields under varying process conditions. The transferred arc energy distribution becomes particularly
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are among the world leaders in through-life approaches for high value systems, Condition monitoring, Damage tolerance, Asset management. TES was developed with the support of EPSRC grant of £ 11 million with
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experts in the prognostics and condition monitoring field, as well as being part of our strong and dynamic research centre at Cranfield University. About the host University/Centre Cranfield is an
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within fusion reactors, especially plasma-facing materials (PFMs) exposed to intense heat fluxes and energetic particles. Understanding and predicting how these materials degrade under such conditions is
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diagnosis and prognosis technologies, and, consequently, improve maintenance decision making. Currently, machine learning exists as the most promising technologies of big data analytics in industrial problems
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, these systems serve as complex functional approximators trained over an input-output data set. ‘Second Wave AI’ is the term used to describe the current glut of 'machine learning' style intelligence, where